Edge caching for cognitive applications

ABSTRACT

For caching of cognitive applications in a communication network a first input signal from a sensor device is detected by a proxy having a cache associated therewith. A representation of the first input signal is computed and sent to a server. A handle function is applied to the representation of the first input signal to compute a first handle value corresponding to the first input signal. The representation of the first input signal is transformed using a cognitive processing model of an answer function to compute a first answer value. A content of the cache is modified by the proxy by storing the first answer value in association with the first handle value in the cache.

TECHNICAL FIELD

The present invention relates generally to a method, system, andcomputer program product for caching within a communication network.More particularly, the present invention relates to a method, system,and computer program product for edge caching for cognitive applicationsin a communication network.

BACKGROUND

In environments where network bandwidth is limited, a network cache canbe used to improve the effective response time between a client and aserver. A network cache is data storage that is located closer to therequester than a server that originally contains the requested data.Caching has been used effectively for applications such asweb-applications, databases and computer processing. Web cachingutilizes temporary storage for storing web documents, such as HTML pagesand images, to reduce bandwidth usage, server load, and/or perceivedlag.

Although web-caching is used frequently, caching has also been usedextensively in other distributed systems, such as information centricnetworks. Caching performs well in these environments because a requestoften includes a unique handle such as a Uniform Resource Identifier(URI). The handle can be used as a unique name that is associated withcached data. Caching works by implementing a proxy or gateway whichallows requests from a client to pass through to a server. When theproxy receives a request containing a handle that has not beenpreviously encountered, the proxy sends the requests to the server, seesthe resulting response and stores the result locally. When a secondrequest with the same handle is encountered by the proxy in the future,the locally stored data is sent by the proxy in a response to the clientinstead of sending the request to the server to get a response from theserver. Various caching implementations are concerned with how to managethe locally cached data, as well as maintaining consistency betweencache and server data.

Cognitive applications are applications which take a request from aclient (which can include a signal from a sensor device, voice samplesfrom a microphone, a request from a computer software, or a signal fromother types of devices) and apply machine learning techniques to producean output. In particular implementations, the output is the result offeeding the signal received from the sensor device as an input to acognitive engine (e.g., a neural network, a decision tree, a rule engineetc.). Examples of input signals include a machine audio signal, a videosignal, or other sensor output signals. A typical machine learningapplication is a classification system in which an input signal, such asa measured audio signal, is classified by machine learning techniquesinto different categories, e.g. a normal sound, breaking glass, brokengear, etc.

In a typical system for a cognitive application a sensor generates asignal and sends the signal to a server over a network. When the serverreceives the signal as an input signal, the server compares the inputsignal against a model stored locally by the model. In someapplications, the model may have been previously learned using trainingdata and may take the form of a neural network, a decision table or arule set, or other modeling implementations. In a typicalimplementation, the server compares input signal x against the model,and computes a function f(x). The server then uses the result of thecomputation of f(x) for a subsequent action such as sending anotification, opening a trouble ticket etc. In some other cases, theserver may be using many cognitive applications in a sequence.

However, in some situations, the network between the sensor and theserver has a high degree of latency or limited bandwidth. In other case,privacy or regulations may prevent input from being sent over thenetwork. The illustrative embodiments recognize that in these and manyother cases, approaches to avoid utilizing the network to access adistant data source by using a more local data source, e.g. by using aproxy server, are desirable. In addition, it may be desirable toconfigure the proxy to perform other processing on the input signal.However, in many cases the proxy may not be able to use the networkmodel. The network model may be too big, or may require some additionaldata at the server.

The illustrative embodiments recognize that in the case of manycognitive applications that are based on machine learning techniques,straight-forward caching is ineffective. The input signals may contain alot of noise, and two consecutive requests do not exactly correspond tothe same input. The signal does not contain a unique handle, and eventhe same signal (e.g. the sound of an engine or the image of a device)may be slightly different over different readings. As a result, theillustrative embodiments recognize that the input in its raw formatwould rarely match, and caching does not work effectively. As a result,it is hard to create effective caches for cognitive applications.

Various embodiments described herein provide for an improvement in theeffectiveness of caching for cognitive applications by configuring aproxy server to use a handle function to generate a handle for areceived signal to assist in caching the input signals in a cacheassociated with the proxy server.

SUMMARY

The illustrative embodiments provide a method, system, and computerprogram product for caching of cognitive applications in a communicationnetwork. An embodiment of a method includes detecting, by a proxy havinga cache associated therewith, a first input signal from a sensor device.The embodiment includes computing a representation of the first inputsignal, the representation being sent to a server. The embodimentincludes applying a handle function to the representation of the firstinput signal to compute a first handle value corresponding to the firstinput signal. The embodiment further includes transforming therepresentation of the first input signal using a cognitive processingmodel of an answer function to compute a first answer value. Theembodiment includes modifying, by the proxy, a content of the cache bystoring the first answer value in association with the first handlevalue in the cache. An advantage of the embodiment is that theembodiment provides for an improvement in the effectiveness of cachingfor cognitive applications through the use of a proxy to generate ahandle for received input signals to assist in caching the inputsignals.

Another embodiment further includes detecting, by the proxy, a secondinput signal. The embodiment includes computing, by the proxy, acandidate handle value using the handle function upon the second inputsignal. The embodiment includes comparing the candidate handle value tothe first handle value stored in the cache. The embodiment furtherincludes determining whether the candidate handle value matches thefirst handle value. An advantage of the embodiment is that theembodiment enables the proxy to determine whether a candidate handlevalue matches a stored handle value in the cache without communicatingwith the server, thereby conserving bandwidth and computing resourceswithin the network.

Another embodiment further includes using, by the proxy, the firstanswer value for the second input signal responsive to determining thatthe candidate handle value matches the first handle value. An advantageof the embodiment is that the embodiment enables the proxy to use ananswer value stored in the cache rather than retrieving the answer valuefrom the server, thereby conserving bandwidth and computing resourceswithin the network.

Another embodiment further includes sending a representation of thesecond input signal to the cognitive server responsive to determiningthat the candidate handle value does not match the first handle value.An advantage of the embodiment is that the embodiment enables the proxyto send a representation of the input signal rather than the inputsignal itself to the server, thereby conserving bandwidth and computingresources within the network.

Another embodiment further includes receiving a second handle value forthe second input signal, in which the second handle value is computed byapplying the handle function to the representation of the second inputsignal. The embodiment further includes receiving a second answer valuecorresponding to the representation of the second input signal, in whichthe second answer value is determined by transforming the representationof the second input signal using the cognitive processing model of theanswer function to compute the first value. The method further includesmodifying, by the proxy, the contents of the cache by storing, thesecond answer value in association with the second handle value in thecache. An advantage of the embodiment is that the embodiment enables theproxy to update the cache associated with the proxy with updated valuesto improve the hit rate of the cache.

In another embodiment determining whether the candidate handle valuematches the first handle value includes performing a nearest neighborsearch between the candidate handle value and the first handle value. Anadvantage of the embodiment is that the embodiment allows for using anearest neighbor search to improve the efficiency of the cache search bythe proxy.

In another embodiment, the first input signal is representative of ameasurement by the sensor device within an environment of the sensordevice. An advantage of the embodiment is that the embodiment providesfor processing of input signals located in a particular environment toidentify particular input signals measured in the environment.

In another embodiment, the handle function is configured to produce thehandle value as a lower dimensional representation of the input signalthan a dimension of the original input signal. An advantage of theembodiment is that the embodiment provides for conserving computingresources within the network due to the lower dimensional representationrequiring fewer computing resources to process.

In another embodiment, the representation of the first input signalincludes the first handle value determined at the proxy. An advantage ofthe embodiment is that the embodiment allows for determining of thehandle value at the proxy, rather than requiring the input signal to besent to the server for determining the handle value, thereby conservingbandwidth within the network.

Another embodiment further includes receiving, by the proxy, anindication of the handle function from the server. An advantage of theembodiment is that the embodiment provides for determining the handlefunction at the server rather than at the proxy, thereby requiring lesscomputing resources by the proxy.

Another embodiment further includes sending a domain name associatedwith a domain of the sensor device to the server in which the serverdetermines the handle function from a plurality of handle functionsbased upon the domain name. An advantage of the embodiment is that theembodiment provides for the server to determine the handle function thatbest suits the input signals expected at a particular domain, therebyimproving the accuracy and efficiency of identifying signals within thedomain.

Another embodiment further includes determining a reconstruction erroron the first input signals using the handle function. The embodimentfurther includes comparing a distribution of the reconstruction error ofthe first input signal to a distribution of reconstructions errors intraining data associated with the cognitive model. The embodimentfurther includes triggering a cache invalidation operation upon thecache responsive to determining that the comparison of the distributionof reconstruction errors exceeds a predetermined threshold value. Anadvantage of the embodiment is that the embodiment provides forinvalidation of the cache associated with the proxy to ensure that thecache remains updated with the most recent answer values, therebyimproving the hit rate of the cache.

Another embodiment includes a computer program product for caching ofcognitive applications in a communication network. An advantage of theembodiment is that the embodiment provides for an improvement in theeffectiveness of caching for cognitive applications through the use of aproxy to generate a handle for received input signals to assist incaching the input signals.

Another embodiment includes a computer system for caching of cognitiveapplications in a communication network. An advantage of the embodimentis that the embodiment provides for an improvement in the effectivenessof caching for cognitive applications through the use of a proxy togenerate a handle for received input signals to assist in caching theinput signals.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofthe illustrative embodiments when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented;

FIG. 2 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 depicts a block diagram of a communication network in whichillustrative embodiments may be implemented;

FIG. 4A depict an example messaging flow between a proxy server and acognitive server according to an illustrative embodiment;

FIG. 4B depict an example messaging flow between a proxy server and acognitive server according to an illustrative embodiment; and

FIG. 5 depicts a flowchart of an example process associated with theproxy server according to an illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments described herein generally relate to edgecaching for cognitive applications. As previously discussed, cognitiveapplications are computer processing applications which take a signalfrom a sensor device and apply machine learning techniques to a set oftraining data to produce an output. In accordance with one or moreembodiments, a caching protocol is implemented between a proxy serverhaving an associated local cache and a cognitive server, in which thecognitive server returns not only the result of applying an input signalon a cognitive model, but also implements a handle generation functionor encoding function configured to generate a unique handle for an inputsignal. In one or more embodiments, the handle generator function orencoding function is computed on each input signal received from asensor device to create a unique handle for the input signal, and theproxy server stores the result received from the cognitive server inassociation with the handle in the cache.

In the embodiment, when the proxy server receives a new input signalfrom a sensor device, the proxy server computes a new handle for the newinput signal using the same handle function used to compute the handlefor the initial input signal, and determines whether the new handlematches the handle stored in the cache using predetermined matchingcriteria. If a match is found, the proxy server using the resultassociated with handle as the result for the new input signal. In one ormore embodiments, the hit rate of the cache is improved through use ofthe unique handle. If a match is not found, the proxy server forwardsthe new input signal to the cognitive server, and the cognitive servercomputes a result and a handle for the new input signal. The cognitiveserver sends the new result and new handle to the proxy server. Theproxy server then stores the new handle and new result or answer valuewithin the cache associated with the proxy server. In accordance withone or more embodiments, a state of the cache is changed because thestoring of the answer value modifies whatever is residing the cacheprior to the storing. When a new input signal is received by the proxyserver having a computed handle value matching a handle value stored inthe cache, the proxy server can use the answer value associated with thematching handle value rather than requiring the cognitive server todetermine the answer value for the new input signal.

Accordingly, one or more embodiments described herein provide for animprovement in the effectiveness of caching for cognitive applicationsthrough the use of a proxy server to generate a handle for receivedinput signals to assist in caching the input signals.

In one or more embodiments, the handle function used to generate ahandle value for a particular input signal received from a sensor deviceis chosen according to one or more properties of the input signal. In aparticular embodiment, the handle function is used to produce a lowerdimensional representation of the input signal than a dimension of theoriginal input signal, and the answer values are indexed by the lowerdimensional representation within the cache of the proxy server. In theparticular embodiment, the proxy server performs a nearest neighborsearch on the indexed values to determine whether there is a cache hitor cache miss. Upon a cache hit, the proxy server uses the answer valuecorresponding to the matched handle value. Upon a cache miss, the proxyserver sends either the raw input signal or its lower dimensionalrepresentation to the cognitive server.

In another particular embodiment, the proxy server uses the handlefunction to trigger cache invalidation. Cache invalidation refers to aprocess in which entries in the cache are replaced or removed becausethey are no longer valid. In the particular embodiment, the proxy serveruses the handle function to determine a reconstruction error on one ormore input signals. The proxy server then compares the distribution ofthe reconstruction errors of the input signals to a distribution ofreconstructions errors in training data. In a particular embodiment, theproxy server uses a Kullback-Leibler (KL) divergence measure todetermine how the probability distribution of the reconstruction errorsof the input signals differs from the probability distribution of thereconstruction errors of training data. If the distribution ofreconstruction errors exceeds a predetermined threshold value, the proxyserver triggers a cache invalidation operation upon the cache.

In another particular embodiment, the proxy server is configured toperform cache revalidation using the handle function. In such anembodiment, the proxy server retrains the model function and handlefunction at the cache. In another particular embodiment, the proxyserver may periodically merge retrained model functions and handlefunctions from multiple caches (e.g., at a centralized cloud).

The illustrative embodiments are described with respect to certain typesof input signals, handle functions, cognitive processing algorithms,cognitive processing models, neural networks, devices, data processingsystems, environments, components, and applications only as examples.Any specific manifestations of these and other similar artifacts are notintended to be limiting to the invention. Any suitable manifestation ofthese and other similar artifacts can be selected within the scope ofthe illustrative embodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the invention, either locally at a dataprocessing system or over a data network, within the scope of theinvention. Where an embodiment is described using a mobile device, anytype of data storage device suitable for use with the mobile device mayprovide the data to such embodiment, either locally at the mobile deviceor over a data network, within the scope of the illustrativeembodiments.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the clarity of the description. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures therefor, may be used inconjunction with such embodiment of the invention within the scope ofthe invention. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

With reference to the figures and in particular with reference to FIGS.1 and 2, these figures are example diagrams of data processingenvironments in which illustrative embodiments may be implemented. FIGS.1 and 2 are only examples and are not intended to assert or imply anylimitation with regard to the environments in which differentembodiments may be implemented. A particular implementation may makemany modifications to the depicted environments based on the followingdescription.

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented. Data processingenvironment 100 is a network of computers in which the illustrativeembodiments may be implemented. Data processing environment 100 includesnetwork 102. Network 102 is the medium used to provide communicationslinks between various devices and computers connected together withindata processing environment 100. Network 102 may include connections,such as wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network 102 and are not intended to exclude otherconfigurations or roles for these data processing systems. Proxy server104 and cognitive server 106 couple to network 102 along with storageunit 108. In one or more embodiments, storage 108 may be configured tostore training data 109 for training one or more cognitive modelsassociated with one or more of proxy server 104 and cognitive server106. Software applications may execute on any computer in dataprocessing environment 100. Clients 110, 112, and 114 are also coupledto network 102. A data processing system, such as server 104 or 106, orclient 110, 112, or 114 may contain data and may have softwareapplications or software tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 1 depicts certain components that are usable in anexample implementation of an embodiment. For example, proxy server 104,cognitive server 106, and clients 110, 112, 114, are depicted as serversand clients only as example and not to imply a limitation to aclient-server architecture. As another example, an embodiment can bedistributed across several data processing systems and a data network asshown, whereas another embodiment can be implemented on a single dataprocessing system within the scope of the illustrative embodiments. Dataprocessing systems 104, 106, 110, 112, and 114 also represent examplenodes in a cluster, partitions, and other configurations suitable forimplementing an embodiment.

Proxy application 105 of proxy server 104 implements an embodiment of aproxy function configured to perform one or more functions of proxyserver 104 as described herein including, but not limited to, receivinginput signals from one or more sensor devices, computing handlefunctions on the input signals to determine handle values, and receivinganswer values from cognitive server 106. Cognitive application 107 ofcognitive server 106 is configured to perform one or more functions ofcognitive server 106 within data processing system 100 as describedherein including, but not limited to, receiving input signals from proxyserver 104, computing an answer function and a handle function upon theinput signals, and sending answer values and handle values to proxyserver 104.

Cache 130 is an example of a storage cache described herein. Cache 130is associated with proxy server 104 and configured to store one or moreof answer values and handle values received from proxy server 104. Inparticular embodiments, cache 130 is a local cache to proxy server 104.

Sensor device 132 is an example of a sensor device described herein. Forexample, sensor device 132 may generate a measurement within theenvironment of sensor device 132 and send a signal, such as an audio orvideo signal, representative of the measurement to proxy server 104.

Proxy servers 104, cognitive server 106, storage unit 108, clients 110,112, and 114, cache 130, and sensor device 132 may couple to network 102using wired connections, wireless communication protocols, or othersuitable data connectivity. Clients 110, 112, and 114 may be, forexample, personal computers or network computers.

In the depicted example, cognitive server 106 may provide data, such asboot files, operating system images, and applications to clients 110,112, and 114. Clients 110, 112, and 114 may be clients to cognitiveserver 106 in this example. Clients 110, 112, 114, or some combinationthereof, may include their own data, boot files, operating systemimages, and applications. Data processing environment 100 may includeadditional servers, clients, and other devices that are not shown.

In the depicted example, data processing environment 100 may be theInternet. Network 102 may represent a collection of networks andgateways that use the Transmission Control Protocol/Internet Protocol(TCP/IP) and other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,governmental, educational, and other computer systems that route dataand messages. Of course, data processing environment 100 also may beimplemented as a number of different types of networks, such as forexample, an intranet, a local area network (LAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

Among other uses, data processing environment 100 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system. Dataprocessing environment 100 may also employ a service orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.Data processing environment 100 may also take the form of a cloud, andemploy a cloud computing model of service delivery for enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (e.g. networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service.

With reference to FIG. 2, this figure depicts a block diagram of a dataprocessing system in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as servers104 and 106, or clients 110, 112, and 114 in FIG. 1, or another type ofdevice in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

Data processing system 200 is also representative of a data processingsystem or a configuration therein, such as data processing system 132 inFIG. 1 in which computer usable program code or instructionsimplementing the processes of the illustrative embodiments may belocated. Data processing system 200 is described as a computer only asan example, without being limited thereto. Implementations in the formof other devices, such as device 132 in FIG. 1, may modify dataprocessing system 200, such as by adding a touch interface, and eveneliminate certain depicted components from data processing system 200without departing from the general description of the operations andfunctions of data processing system 200 described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)202 and South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 arecoupled to North Bridge and memory controller hub (NB/MCH) 202.Processing unit 206 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems.Processing unit 206 may be a multi-core processor. Graphics processor210 may be coupled to NB/MCH 202 through an accelerated graphics port(AGP) in certain implementations.

In the depicted example, local area network (LAN) adapter 212 is coupledto South Bridge and I/O controller hub (SB/ICH) 204. Audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) and other ports 232, and PCI/PCIe devices 234are coupled to South Bridge and I/O controller hub 204 through bus 238.Hard disk drive (HDD) or solid-state drive (SSD) 226 and CD-ROM 230 arecoupled to South Bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM 230may use, for example, an integrated drive electronics (IDE), serialadvanced technology attachment (SATA) interface, or variants such asexternal-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device236 may be coupled to South Bridge and I/O controller hub (SB/ICH) 204through bus 238.

Memories, such as main memory 208, ROM 224, or flash memory (not shown),are some examples of computer usable storage devices. Hard disk drive orsolid state drive 226, CD-ROM 230, and other similarly usable devicesare some examples of computer usable storage devices including acomputer usable storage medium.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within dataprocessing system 200 in FIG. 2. The operating system may be acommercially available operating system for any type of computingplatform, including but not limited to server systems, personalcomputers, and mobile devices. An object oriented or other type ofprogramming system may operate in conjunction with the operating systemand provide calls to the operating system from programs or applicationsexecuting on data processing system 200.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as applications 105 in FIG.1, are located on storage devices, such as in the form of code 226A onhard disk drive 226, and may be loaded into at least one of one or morememories, such as main memory 208, for execution by processing unit 206.The processes of the illustrative embodiments may be performed byprocessing unit 206 using computer implemented instructions, which maybe located in a memory, such as, for example, main memory 208, read onlymemory 224, or in one or more peripheral devices.

Furthermore, in one case, code 226A may be downloaded over network 201Afrom remote system 201B, where similar code 201C is stored on a storagedevice 201D. In another case, code 226A may be downloaded over network201A to remote system 201B, where downloaded code 201C is stored on astorage device 201D.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS.1-2. In addition, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache, such as the cache found inNorth Bridge and memory controller hub 202. A processing unit mayinclude one or more processors or CPUs.

The depicted examples in FIGS. 1-2 and above-described examples are notmeant to imply architectural limitations. For example, data processingsystem 200 also may be a tablet computer, laptop computer, or telephonedevice in addition to taking the form of a mobile or wearable device.

Where a computer or data processing system is described as a virtualmachine, a virtual device, or a virtual component, the virtual machine,virtual device, or the virtual component operates in the manner of dataprocessing system 200 using virtualized manifestation of some or allcomponents depicted in data processing system 200. For example, in avirtual machine, virtual device, or virtual component, processing unit206 is manifested as a virtualized instance of all or some number ofhardware processing units 206 available in a host data processingsystem, main memory 208 is manifested as a virtualized instance of allor some portion of main memory 208 that may be available in the hostdata processing system, and disk 226 is manifested as a virtualizedinstance of all or some portion of disk 226 that may be available in thehost data processing system. The host data processing system in suchcases is represented by data processing system 200.

With respect to FIG. 3, FIG. 3 depicts a block diagram of acommunication network 300 in which illustrative embodiments may beimplemented. The communication network 300 includes sensor device 132 incommunication with proxy server 104 having associated local cache 130.Proxy server 104 is in further communication with cognitive server 106.

In one or more embodiments, proxy server 104 receives an input signal xfrom sensor device 132, and proxy server forwards the input signal x tocognitive server 106. In response to receiving the input signal x,cognitive server 106 computes an answer value from the input signal xusing an answer function f(x). In particular embodiments, the answerfunction f(x) processes the input signal x using a cognitive processingmodel to generate a resulting answer value. In one or more embodiments,the cognitive processing model operates to transform a representation ofthe input signal using an answer function to compute an answer value. Inparticular embodiments, the cognitive processing model may include aneural network trained using training data (such as training data 109 ofFIG. 1), a decision tree, a rule engine, a classification algorithm orany other cognitive processing technique. In particular embodiments, theanswer value may include, for example, a classification oridentification of the input signal x, an indication of furtherprocessing of the input signal x, or an indication of an action to beperformed by cognitive server 106 or another computer or data processingsystem in response to receiving the input signal x. In a particularembodiment, the action includes sending a notification of the inputsignal x to a client device.

In one or more embodiments, cognitive server 106 is further configuredto compute a handle value for the input signal x using a handle functiong(x). In one or more embodiments, the handle function g(x) may includeany function that may be used to compute a handle for a given inputsignal x based on the nature or property of the input signal x. Inparticular embodiments, the handle function g(x) may include one or moreof a shallow neural network trained on the signal, an auto-encoder thatextracts one or more features from the input signal, or a hash function.

In an embodiment, cognitive server 106 is further configured to send theanswer value and the handle value to proxy server 104, and proxy server104 is configured to store the answer value in association with thehandle value within cache 130. In one or more embodiments, the handlefunction g(x) is also known to proxy server 104 as well as cognitiveserver 106. When proxy server 104 receives a new input signal y fromsensor device 132, proxy server 104 computes the handle function g(y) onthe input signal y to obtain a new handle value for the input signal y.Proxy server 104 compares the resulting handle value to previouslystored handle function values within cache 130, for example the handlevalue resulting from g(x), to determine if the new handle value matchesa stored handle value according to predetermined matching criteria. Ifthe new handle value obtained from g(y) matches a stored handle valueobtained from g(x) previously received from the server, the previouslystored answer value of the answer f(x) is used as the answer value tothe input signal y.

In one embodiment, the handle function g(x) is predetermined at proxyserver 104, and proxy server 104 applies the handle function g(x) toinput signal x to determine a handle value. Proxy server 104 sends theoriginal input signal x to cognitive server 106. In an embodiment,cognitive server 106 is further configured to compute an answer valueusing answer function f(x) and send the answer value to proxy server104. Proxy server 104 is configured to store the answer value inassociation with the handle value within cache 130. Proxy server 104uses the handle value to check for the presence of a locally cachedhandle value and an associated answer value within cache 130, and usesthe cached answer value as the answer value to a input signal having ahandle value matching the associated cached handle value. An advantageto such an embodiment is that it may be implemented without makingchanges to an existing cognitive server.

In another embodiment, the handle function g(x) is predetermined atproxy server 104, and proxy server 104 applies the handle function g(x)to an input signal x to determine a handle value. Proxy server 104 usesthe handle value to check for the presence of a locally cached handlevalue and an associated answer value, and uses the cached answer valueas the answer value to a input signal having a handle value matching theassociated cached handle value. Proxy server 104 further sends thecomputed handle value to cognitive server 106 using a communicationprotocol between proxy server 104 and cognitive server 106, andcognitive server 106 performs cognitive processing, such asclassification of the input signal, using the handle value computed byproxy server 104 rather than the input signal x. An advantage of such anembodiment is that it avoids computation of the handle function g(x) atboth proxy server 104 and cognitive server 106 saving computationalresources within communication network 300.

In another embodiment, sensor device 132 is associated with a particulardomain such as a particular location or environment in which sensordevice 132 is allocated. Proxy server 104 associates a domain name withthe domain of sensor device 132. In a particular embodiment, proxyserver 104 sends the domain name to cognitive server 106 indicating thatproxy server 104 serves the domain associated with the domain name. Inresponse to receiving the domain name, cognitive server 106 determinesthe handle function g(x) that is optimal for serving the identifieddomain and sends a definition and/or indication of the handle functiong(x) to proxy server 104. In an embodiment, cognitive server 106determines a best handle function g(x) for the domain that can beperformed based upon available training data. In a particularembodiment, hand function g(x) may include a trained neural network thatis smaller or less complex than a neural network implemented bycognitive server 106 for the function f(x). In other particularembodiments, the determined handle function g(x) may include a featureextractor or signal encoder.

Upon receiving the definition or identification of the handle functiong(x), proxy server 104 will utilize the handle function g(x) upon inputsignals received from sensor device 132. Upon receiving an input signalx from sensor device 132, proxy server 104 applies the handle functiong(x) associated with the domain to the input signal x to determine ahandle value. Proxy server 104 uses the handle value to check for thepresence of a locally cached handle value and an associated answervalue, and uses the cached answer value as the answer value to a inputsignal having a handle value matching the associated cached handlevalue. Proxy server 104 further sends the computed handle value tocognitive server 106 using a communication protocol between proxy server104 and cognitive server 106, and cognitive server 106 performscognitive processing, such as classification of the input signal, usingthe handle value computed by proxy server 104 rather than the inputsignal x. Cognitive server 106 is further configured to compute ananswer value using answer function f(x) and send the answer value toproxy server 104. Proxy server 104 is configured to store the answervalue in association with the handle value within cache 130. Proxyserver 104 uses the handle value to check for the presence of a locallycached handle value and an associated answer value within cache 130, anduses the cached answer value as the answer value to a input signalhaving a handle value matching the associated cached handle value. Anadvantage of such an embodiment is that the handle function g(x) used tocompute the handle value corresponding to an input signal x may bechosen by cognitive server 106 to be optimized according to a particulardomain associated with sensor device 132.

In another embodiment, proxy server 104 receives the original inputsignal x from sensor device 132 and sends the original input signal x tocognitive server 106. Cognitive server 106 computes the handle functiong(x) on the inputs signal x to determine a handle value, and sends thehandle value to proxy server 104. Proxy server 105 determines whetherthe handle value is stored locally within cache 130. If the handle valueis stored locally, proxy server 104 uses the answer value associatedwith the handle value. If the handle value is not stored locally, proxyserver 104 sends a request to cognitive server 106 to for the answervalue associated with the handle value. Cognitive server 106 is furtherconfigured to compute an answer value using answer function f(x) andsend the answer value to proxy server 104. Proxy server 104 receives theanswer value and stores the answer value and handle value within cache130. Although such an embodiment requires additional messaging betweenproxy server 104 and cognitive server 106 when the handle value is notcurrently stored in cache 130, processing at proxy server 104 issignificantly reduced.

In one embodiment, proxy server 104 and sensor device 132 may belong toand be operated by an organization that is different from theorganization that is operating the cognitive server 106. As an example,proxy server 104 may be operated by a manufacturing plant thatmanufactures products and cognitive server 106 may be a cloud serviceoperated by a cloud server provider. The sensor device 132 may be acamera. In the particular embodiment, the manufacturing plant providesthe cloud service with training data that consists of several imagesthat show what an acceptable product looks like and what typical defectsin the products look like. In the embodiment, the cloud service uses theimages to build a model, such as a neural network, for identifying goodproducts and bad products. In the embodiment, the manufacturing plantuses the camera as sensor device 132 to send images of units producedduring the manufacturing process to proxy server 104 which identifiesgood products and bad products, leveraging the model from cognitiveserver 106 in the cloud. In the particular embodiment, only one requestfor each type of defect or good product goes to the cloud service,enabling the manufacturing plant to keep its sensitive operational data,such as the total number of units produced and defect rates in itsmanufacturing process, private by not exposing the operational data tothe cloud service. At the same time, the capabilities of the cloudservice are used for training the model.

In another embodiment, the manufacturing plant may be using a microphoneas sensor device 132, and using sounds produced by a unit during testingprocess to determine if a product is good or defective, such as checkingfor proper operation of a jet engine, a washing machine, a dryer, or anair-conditioner. In the embodiment, sounds of good and bad units areprovided to the cloud service implementing the cognitive server 106 totrain its models based on decision trees. In the embodiment, the soundsare checked by proxy server 106 without exposing sensitive informationto the cloud service. Sensitive information, which may includeoperational data such as the total number of units produced and defectrates in its manufacturing process, are kept private, by not exposingthe sensitive information to the cloud service.

In another embodiment, a bank may use a cognitive server 106 in thecloud provided by another cloud service operator to classify its loandocuments into several levels of risk worthiness. Typical examples ofdifferent documents in different categories are provided to the cloudservice which produces a neural network model to classify differentdocuments into their corresponding risk categories. In the embodiment,the training process also trains a shallow model to define a handlefunction to characterize each document. In the embodiment, the bank usesthe database server with loan documents as sensor device 132, and proxyservice 106 to classify documents within the bank's control. In theembodiment, the bank avoids sending several documents to the cloud basedservice for classification.

With reference to FIGS. 4A-4B, these figures depict an example messagingflow 400 between proxy server 104 and cognitive server 106 according toan illustrative embodiment. In 402, sensor device 132 sends a firstinput signal x to proxy server 104. In 404, proxy server 104 sends thefirst input signal x to cognitive server 106. In response to receivingthe input signal x, in 406 cognitive server 106 computes a first answervalue from answer function f(x). In particular embodiments, the answerfunction f(x) processes the first input signal x using a cognitiveprocessing model to generate a resulting answer value. In 408, cognitiveserver 106 computes a first handle value corresponding to the firstinput signal x using a handle function g(x). In 410, cognitive server106 sends the first answer value and first handle value to proxy server104. In 412, proxy server 104 stores the first answer value inassociation with the first handle value within cache 130.

In 414, proxy server 104 receives a second input signal y from sensordevice 132. In 416, proxy server 104 computes a new handle value on theinput signal y using the handle function g(x). In 418, proxy server 104compares the new handle value to the stored first handle value withincache 130. In one or more embodiments, cache 130 may include a pluralityof answer values and associated handle values obtained from previouslyreceived input signals. In 420, if a match is found, proxy server usesthe stored first answer value corresponding to the matching storedhandle value as the answer for the input signal y. In 422, proxy server104 sends the new input signal y to cognitive server 422 and themessaging flow 400 ends.

With reference to FIG. 5, this figure depicts a flowchart of an exampleprocess 500 associated with proxy server 104 according to anillustrative embodiment. In block 502, proxy server 104 receives a firstinput signal x from sensor device 132. In block 504, proxy server 104sends the first input signal x to cognitive server 106. In response toreceiving the input signal x, cognitive server 106 computes a firstanswer value from answer function f(x). Cognitive server 106 furthercomputes a first handle value corresponding to the first input signal xusing a handle function g(x). In block 506, proxy server 104 receivesthe first answer value and first handle value from cognitive server 106.In block 508, proxy server 104 stores the first answer value inassociation with the first handle value within cache 130.

In block 510, proxy server 104 receives a second input signal y fromsensor device 132. In block 512, proxy server 104 computes a candidatehandle value on the second input signal y using the handle functiong(x). In block 514, proxy server 104 compares the candidate handle valueto the stored first handle value within cache 130. In block 516, proxyserver 104 determines if a match is found using predetermined matchingcriteria. In a particular embodiment, proxy server 104 determines thatthe candidate handle value matches the first handle value if adifference between the candidate handle value and the first handle valueis within a predetermined threshold. If a match is found, in block 518proxy server 104 uses the first answer value as the answer to the secondinput signal y and process 500 ends.

If no match is found between the candidate handle value and the firsthandle value, in block 520 proxy server 104 sends the second input valuey to cognitive server 106. In response to receiving the second inputvalue y, cognitive server 106 computes a second answer value from theanswer function and a second handle value from the handle function, andsends the second answer value and second handle value to proxy server104. In block 514, proxy server 104 stores the second answer value inassociation with the second handle value within cache 130. The process500 then ends.

Although various embodiments are described with respect to performingcaching operations between a proxy server and a cognitive server, itshould be understood that the principles described herein may be appliedto any suitable cognitive application performed by a computer system orother electronic device.

Thus, a computer implemented method, system or apparatus, and computerprogram product are provided in the illustrative embodiments foroperations with caching of answer values of cognitive applications andother related features, functions, or operations. Where an embodiment ora portion thereof is described with respect to a type of device, thecomputer implemented method, system or apparatus, the computer programproduct, or a portion thereof, are adapted or configured for use with asuitable and comparable manifestation of that type of device.

Where an embodiment is described as implemented in an application, thedelivery of the application in a Software as a Service (SaaS) model iscontemplated within the scope of the illustrative embodiments. In a SaaSmodel, the capability of the application implementing an embodiment isprovided to a user by executing the application in a cloudinfrastructure. The user can access the application using a variety ofclient devices through a thin client interface such as a web browser(e.g., web-based e-mail), or other light-weight client-applications. Theuser does not manage or control the underlying cloud infrastructureincluding the network, servers, operating systems, or the storage of thecloud infrastructure. In some cases, the user may not even manage orcontrol the capabilities of the SaaS application. In some other cases,the SaaS implementation of the application may permit a possibleexception of limited user-specific application configuration settings.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method for caching of cognitive applications ina communication network comprising: computing a representation of afirst input signal from a sensor device; computing a first handle valuecorresponding to the first input signal; transforming the representationof the first input signal using a cognitive processing model of ananswer function to compute a first answer value; modifying, by a proxy,a content of a cache by storing the first answer value in associationwith the first handle value in the cache; computing, by the proxy, acandidate handle value corresponding to a second input signal; sending arepresentation of the second input signal to a cognitive serverresponsive to determining that the candidate handle value does not matchthe first handle value; receiving a second handle value for the secondinput signal, the second handle value computed by applying a handlefunction to the representation of the second input signal; receiving asecond answer value corresponding to the representation of the secondinput signal, the second answer value being determined by transformingthe representation of the second input signal using the cognitiveprocessing model of the answer function to compute the first value; andmodifying, by the proxy, the contents of the cache by storing, thesecond answer value in association with the second handle value in thecache.
 2. The method of claim 1, further comprising: sending arepresentation of the second input signal to the cognitive serverresponsive to determining that the candidate handle value does not matchthe first handle value.
 3. The method of claim 2, further comprising:receiving a second handle value for the second input signal, the secondhandle value computed by applying the handle function to therepresentation of the second input signal; and receiving a second answervalue corresponding to the representation of the second input signal,the second answer value being determined by transforming therepresentation of the second input signal using the cognitive processingmodel of the answer function to compute the first value; and modifying,by the proxy, the contents of the cache by storing, the second answervalue in association with the second handle value in the cache.
 4. Themethod of claim 1, wherein determining whether the candidate handlevalue matches the first handle value includes performing a nearestneighbor search between the candidate handle value and the first handlevalue.
 5. The method of claim 1, wherein the first input signal isrepresentative of a measurement by the sensor device within anenvironment of the sensor device.
 6. The method of claim 1, wherein thehandle function is configured to produce the handle value as a lowerdimensional representation of the input signal than a dimension of theoriginal input signal.
 7. The method of claim 1, wherein therepresentation of the first input signal includes the first handle valuedetermined at the proxy.
 8. The method of claim 1, further comprising:receiving, by the proxy, an indication of the handle function from aserver.
 9. The method of claim 8, further comprising: sending a domainname associated with a domain of the sensor device to the server, theserver determining the handle function from a plurality of handlefunctions based upon the domain name.
 10. The method of claim 1, furthercomprising: determining a reconstruction error on the first inputsignals using the handle function; comparing a distribution of thereconstruction error of the first input signal to a distribution ofreconstructions errors in training data associated with the cognitivemodel; and triggering a cache invalidation operation upon the cacheresponsive to determining that the comparison of the distribution ofreconstruction errors exceeds a predetermined threshold value.
 11. Themethod of claim 1, wherein the method is embodied in a computer programproduct comprising one or more computer-readable storage mediums andcomputer-readable program instructions which are stored on the one ormore computer-readable storage mediums and executed by one or moreprocessors.
 12. The method of claim 1, wherein the method is embodied ina computer system comprising one or more processors, one or morecomputer-readable memories, one or more computer-readable storagemediums and program instructions which are stored on the one or morecomputer-readable storage mediums for execution by the one or moreprocessors via the one or more memories and executed by the one or moreprocessors.
 13. A computer program product for caching of cognitiveapplications in a communication network, the computer program productcomprising one or more computer-readable storage media and programinstructions stored on at least one of the one or more storage media,the stored program instructions comprising: program instructions tocompute a representation of a first input signal from a sensor device;program instructions to compute a first handle value corresponding tothe first input signal; program instructions to transform therepresentation of the first input signal using a cognitive processingmodel of an answer function to compute a first answer value; programinstructions to modify, by the proxy, a content of a cache by storingthe first answer value in association with the first handle value in thecache; program instructions to compute, by the proxy, a candidate handlevalue corresponding to a second input signal; program instructions tosend a representation of the second input signal to a cognitive serverresponsive to determining that the candidate handle value does not matchthe first handle value; program instructions to receive a second handlevalue for the second input signal, the second handle value computed byapplying a handle function to the representation of the second inputsignal; program instructions to receive a second answer valuecorresponding to the representation of the second input signal, thesecond answer value being determined by transforming the representationof the second input signal using the cognitive processing model of theanswer function to compute the first value; and program instructions tomodify, by the proxy, the contents of the cache by storing, the secondanswer value in association with the second handle value in the cache.14. The computer program product of claim 13, wherein the stored programinstructions further comprise: program instructions to sending arepresentation of the second input signal to the cognitive serverresponsive to determining that the candidate handle value does not matchthe first handle value.
 15. The computer program product of claim 13,wherein the first input signal is representative of a measurement by thesensor device within an environment of the sensor device.
 16. Thecomputer program product of claim 13, wherein the handle function isconfigured to produce the handle value as a lower dimensionalrepresentation of the input signal than a dimension of the originalinput signal.
 17. The computer program product of claim 13, wherein therepresentation of the first input signal includes the first handle valuedetermined at the proxy.
 18. The computer program product of claim 13,wherein the stored program instructions further comprise: programinstructions to receive, by the proxy, an indication of the handlefunction from a server.
 19. The computer program product of claim 18,wherein the stored program instructions further comprise: programinstructions to send a domain name associated with a domain of thesensor device to the server, the server determining the handle functionfrom a plurality of handle functions based upon the domain name.
 20. Acomputer system for caching of cognitive applications in a communicationnetwork, the computer system comprising one or more processors, one ormore computer-readable memories, and one or more computer-readablestorage media, and program instructions stored on at least one of theone or more storage media for execution by at least one of the one ormore processors via at least one of the one or more memories, the storedprogram instructions comprising: program instructions to compute arepresentation of a first input signal from a sensor device; programinstructions to compute a first handle value corresponding to the firstinput signal; program instructions to transform the representation ofthe first input signal using a cognitive processing model of an answerfunction to compute a first answer value; program instructions tomodify, by the proxy, a content of a cache by storing the first answervalue in association with the first handle value in the cache; programinstructions to compute, by the proxy, a candidate handle valuecorresponding to a second input signal; program instructions to send arepresentation of the second input signal to a cognitive serverresponsive to determining that the candidate handle value does not matchthe first handle value; program instructions to receive a second handlevalue for the second input signal, the second handle value computed byapplying a handle function to the representation of the second inputsignal; program instructions to receive a second answer valuecorresponding to the representation of the second input signal, thesecond answer value being determined by transforming the representationof the second input signal using the cognitive processing model of theanswer function to compute the first value; and program instructions tomodify, by the proxy, the contents of the cache by storing, the secondanswer value in association with the second handle value in the cache.